Data in2008

For example, the 2008 ACS 1-year data reflect incomes over 2007-2008 and the 2006-2008 ACS 3-year data reflect incomes over 2005-2008. In a comparison study between Census 2000 income data and the 2000 ACS, income collected in Census 2000 was found to be about 4 percent higher than that in the 2000 ACS. Real GDP,” Select “Modify,” Select “First Year 2008,” Select “Series Quarterly,” Select “Refresh Table.' Accessed June 12, 2020. S&P Dow Jones Indices. 'DJIA Daily Performance Report,' Select 'Excel spreadsheet.' Accessed June 12, 2020. Bureau of Economic Analysis. “Data Archive: National Accounts (NIPA).” Accessed June 12 ... Feature Space Augmentation for Long-Tailed Data Peng Chu 1?, Xiao Bian2, Shaopeng Liu3, and Haibin Ling4; 1 Temple University, USA [email protected] 2 Google Inc., USA [email protected] 3 GE Research, USA [email protected] 4 Stony Brook University, USA [email protected] Abstract. Real-world data often follow a long-tailed distribution as the frequency of each class is typically di erent. This page contains links to data for all figures and tables in This Time Is Different: Eight Centuries of Financial Folly, Princeton University Press, 2009.This data is also located on Carmen Reinhart's website.. The reader will note that the data set is quite massive and can be used to study a huge range of issues. In 2008, the DCS radar was modified to achieve a 5 km diameter spot, while maintaining radar resolution and sensitivity. In August 2008, AFRL performed a data collection with the modified DCS radar along with EO WAMI asset. A fleet of 25 civilian vehicles were deployed at various locations. 2008 Traffic Data. This index is provided as a simple means to locate and open all of the publications available. It contains links to the actual files for each publication. All of the publications are available in the Adobe Acrobat and Microsoft Excel file formats. SIPP 2008 Panel Waves 01-10 - Core Data Dictionary [<1.0 MB] SIPP 2008 Panel Replicate Weight File Data Dictionary [<1.0 MB] Component ID: #ti1498244694. Topical Module Data Dictionaries SIPP 2008 Panel Wave 13 - Topical Module Data Dictionary ...


2011.03.01 01:47 flipmosquad r/23andMe

Talk about your genes and their possible implications! Discord:

2017.08.29 11:02 Vailhem Reuters_Articles

The Reuters_Articles Reddit

2014.07.09 18:27 LL_Cool_Bean Ngram Addicts

A place for addicts of the [Google Ngram Viewer]( to post their new and interesting finds.

2020.08.07 21:21 lolpolice88 Under-represented and overlooked: Māori and Pasifika scientists in Aotearoa New Zealand’s universities and crown-research institutes

"Under-represented and overlooked: Māori and Pasifika scientists in Aotearoa New Zealand’s universities and crown-research institutes

Tara G. McAllister ,Sereana Naepi📷,Elizabeth Wilson📷,Daniel Hikuroa📷 &Leilani A. Walker


This article provides insights into the ethnicity of people employed in Aotearoa New Zealand’s publicly-funded scientific workforce, with a particular focus on Māori and Pasifika scientists. We show that between 2008 and 2018, Māori and Pasifika scientists were severely under-represented in Aotearoa New Zealand’s universities and crown-research institutes. Despite espousals by these institutions of valuing diversity, te Tiriti o Waitangi and Māori research, there have been very little changes in the overall percentage of Māori and Pasifika scientists employed for a period of at least 11 years. Notably, one university reported having not employed a single Māori or Pasifika academic in their science department from 2008 to 2018. We highlight the urgent need for institutions to improve how they collect and disseminate data that speaks to the diversity of their employees. We present data that illustrate that universities and crown-research institutes are failing to build a sustainable Māori and Pasifika scientific workforce and that these institutions need to begin to recruit, retain and promote Māori and Pasifika scientists.


In 2018, Dr Megan Woods (Minister of Research, Science and Innovation) launched the Ministry of Business, Innovation and Employment’s (MBIE) diversity in science statement, which states that ‘Diversity is vital for our science system to realise its full potential’ (MBIE 2018). Whilst this statement is a step towards raising awareness of the importance of diversity in science it needs to be followed by institutional changes, targeted programmes and directed responses from institutions. A vital component of achieving the aspirations espoused in this statement includes open reporting on diversity of ‘applicants, award holders, and advisory, assessment and decision making bodies’ (MBIE 2018). In two recent papers, McAllister et al. (2019) and Naepi (2019) spoke to the lack of diversity in Aotearoa New Zealand 1 ’s eight universities and provided evidence of the severe under-representation of Māori and Pasifika scholars, who comprise 16.5% and 7.5% respectively of the total population of Aotearoa. The authors showed that Māori and Pasifika comprise 4.8% and 1.7% respectively of academics, despite the espousals by universities of valuing diversity and their obligations to equity as outlined in te Tiriti o Waitangi (McAllister et al. 2019; Naepi 2019). The data used in these two studies, obtained from the Ministry of Education (MoE), provided information on the ethnicity of academic staff university wide and was not disaggregated by faculty. Consequently, data on the number of Māori and Pasifika academics in each faculty or department is currently not openly available. Previous research has shown that very few Māori academics exist outside of Māori departments and it remains difficult to access quantitative data on their lived experience as universities continue to silence reports (Kidman et al. 2015; UoO date unknown).
To ensure that the aspirations championed within MBIE’s diversity statement can be met, we first need open and accurate reporting on the diversity of people employed within Aotearoa New Zealand’s scientific workforce and there is currently a significant gap of openly available data that investigate this. Some annual reports and equity profiles of crown-research institutes (CRIs) and universities do contain selected ethnicity data (i.e. MWLR 2018; UoA 2018). However, these reports do not always present data in a meaningful and consistent way and are not always publically available. For example, the University of Otago’s annual report does not contain any information on the ethnicity of staff and instead focuses only on gender of staff and the ethnicity of students (UoO 2018). Instead, the ethnicity data for staff is presented in the equity report, which is only available to staff and access must be requested from the Head of Organisational Development (UoO date unknown).
A survey of Aotearoa New Zealand’s scientists and technologists in 2008 provides the most recent quantitative indication of the diversity of Aotearoa New Zealand’s scientific workforce, despite being conducted 12 years ago (Sommer 2010). The author indicated that there was very little change in ethnicity of Aotearoa New Zealand’s scientific workforce between the 1996 and 2008 surveys, with ‘European’ scientists making up 82.3% and 80.9% respectively (Sommer 2010). According to the author, there was a ‘modest increase’ in Māori scientists from 0.7% (1996) to 1.7% (2008) and this increase ‘represents a glimmer of success for those who have sought to develop policies to bring more Māori into the science and technology workforce’ (Sommer 2010, p. 10). However, an increase of 1% over a period of 15 years (i.e. an average increase of 0.07% per year) should be viewed as a significant failure. The percentage of Pasifika scientists also increased very slightly from 0.5% in 1996 to 0.6% in 2010 (Sommer 2010). McKinley (2002, p. 109) provided an insight into the extremely low numbers of Māori women employed by CRIs in 1998:
‘Of the 3,839 people employed by seven Crown Research Institutes (CRIs) in New Zealand, 57 women or approximately 1.5% of the total identified as Māori women. At the time these data were collected in 1998 there were no Māori women in management positions, two were categorised as scientists, 15 as science technicians, and 40 as ‘support’ staff that includes cafeteria staff, administration staff and cleaners’
The data presented by both McKinley (2002) and Sommer (2010) highlight the urgent need for institutions and government to move away from ‘business as usual’ and make a serious commitment to firstly collecting data on diversity, openly and transparently presenting it and secondly increasing the hiring, promoting and retention of Māori and Pasifika scientists.
The present paper aims to begin to address the gap in knowledge by collating data and investigating how diverse Aotearoa New Zealand’s scientific workforce is. An intersectional lens must be applied when thinking critically about diversity and equity, however policies, actions and research often privilege gender (i.e. Bhopal and Henderson 2019; Brower and James 2020) over ethnicity whilst ignoring other intersectional identities that go beyond white, cis women. Here, we focus on the intersectional identities of Māori and Pasifika scientists, while acknowledging that people who have other intersectional identities including those with disabilities, LGBTQIA, non-binary and women of colour are likely to be disproportionately affected and disadvantaged within Aotearoa New Zealand’s science system, which like universities, was arguably created by and made for white, cis men (Ahmed 2012; Osei-Kofi 2012; Naepi et al. 2017; Akenahew and Naepi 2015). This paper examines the current diversity of Aotearoa New Zealand’s scientific workforce, with a particular focus on Māori and Pasifika. We will address the following questions:
  1. How many Māori and Pasifika scientists are employed in Aotearoa New Zealand’s universities and CRIs?
  2. How has the percentage of Māori and Pasifika scientists in these institutions changed between 2008 and 2018?


Data collection

Data was requested from universities and CRIs by emailing key individuals within each organisation in 2019. Data from 2008 to 2018 on the percentage of scientists, relative to both the total headcount and the total number of full-time equivalents (FTEs) for each recorded ethnicity employed was requested from CRIs and universities. Both the nature of responses to this request and the time it took to receive a response varied among institutions. Responses from institutions ranged from an openness and willingness to contribute data to this project to hostility and racist remarks. Several institutions did not respond to multiple email requests. A subsequent email sent by a Principal Advisor from the Office of the Prime Minister’s Chief Science Advisor elicited a prompt response from all remaining institutions. After initial conversations with staff from HR departments and university management, it was agreed that all institutions would remain anonymous and we believe that this contributed significantly to increasing the willingness of institutions to contribute data. Overall, data was obtained from 14 out of 15 of Aotearoa New Zealand’s universities and CRIs. At most of these institutions staff self-declare their ethnicities and are given multiple choices, where data was provided for multiple ethnicities we used the first reported ethnicity,

Data from universities

Seven out of eight universities contributed data directly to this project, whereas data for university B was extracted from annual reports. Ethnicity data in the form of FTEs and headcount data was provided by most universities. Māori and Pasifika academics are more likely to be employed on contracts of less than one FTE compared to Pākehā academics (unpublished data). We therefore present the percentage of FTEs of staff for each recorded ethnicity, rather than headcount data as it is likely to be a more accurate measure of diversity. Recorded ethnicity groups differed among some universities, mainly in the fact that some distinguished between ‘European’ and ‘NZ European/Pākehā’, whereas at others these two ethnicities were combined.
It is important to note that the data from universities presented in this paper includes academic staff and excludes research staff, including post-doctoral fellows and laboratory technicians. Data on the number of scientists employed at universities also only includes scientists employed in science departments (i.e. excludes Māori scientists in health departments). However, a recent paper published by Naepi et al. (2020) showed that in 2017, there were only 55 Māori and 20 Pasifika postdoctoral fellows across all faculties in all of Aotearoa New Zealand’s universities. The number of Māori and Pasifika postdoctoral fellows employed in science faculties is, therefore, likely to be very small. Academic staff includes other academic staff, senior tutors, tutors, tutorial assistants, lecturers, senior lecturers, associate professors and professors. Previous research has shown that a large proportion of Māori and Pasifika academics are employed as tutors and other academic staff rather than in permanent senior academic positions (see Naepi 2019), so this is also likely to be the case within science faculties.
Concerningly, two universities (university E and H) were unable to provide data for the requested 11-year period (i.e. from 2008 to 2018). Upon querying this with human resource (HR) departments, their reasons included but were not limited to the following:

Data from crown-research institutes

Data, in some shape or form, was obtained from six out of seven of Aotearoa New Zealand’s CRIs. Obtaining accurate and consistent temporal data from CRIs was, despite their willingness, much more difficult than from universities. The MoE requires certain ethnicity data from universities in a particular format (see MoE date unknown), however the diversity of staff employed at Aotearoa New Zealand’s seven CRIs is currently not required by an external organisation. Most CRIs were unable to provide FTE data but were able to provide headcount data, consequently we present the headcount data in this report. Because the data from CRIs was highly variable, we were not prescriptive about how they defined a scientist, however at most institutions this included post-doctoral fellows and scientists.
Data on the percentage of Māori and Pasifika scientists employed from 2008 to 2018 could only be obtained from four out of seven of the CRIs. CRI F could only provide ethnicity for staff that were recent hires from 2016 to 2018, meaning we are unable to differentiate between science and non-science staff and data on staff employed prior to 2016 was unavailable. CRI E could only provide data for 2019, the year that we had asked for it, due to their HR system overwriting data and therefore having no historical record of staff ethnicity.
The ethnicity data from CRIs, with the exception of CRI B, can only be viewed as indicative due to inconsistencies in how CRIs collect data. Data from most institutions was therefore not conducive to any temporal or statistical analyses. For example, at CRI A over the 11-year period, the ethnicity categories offered to staff changed four times. Māori and Pasifika were consistently given as options, which provides some level of confidence in CRI A’s ethnicity data.


Māori scientists employed in Aotearoa New Zealand’s universities

Before even considering the data presented below, we must acknowledge and highlight that science faculties within universities are generally not safe and inclusive environments for Māori and Pasifika academic staff. Reasons for this include that being the only Indigenous person in a faculty puts that one under extreme pressure to help colleagues, indigenise curriculum, support Indigenous students while also advancing their own career (Mercier et al. 2011; Kidman et al. 2015). It is well established that the job satisfaction of Māori academics is influenced by their proximity to other Māori academics (Mercier et al. 2011; Kidman et al. 2015). The interdisciplinary work of Māori scientists also often does not align with what the academy and their Pākehā counterparts define as ‘science’ and many scholars have explored this (see for example, McKinley 2005; Mercier 2014; Hikuroa 2017). Consequently, of the few Māori scientists that exist and survive within academia, several are employed outside of science faculties (see for example, Mercier 2014). This data therefore is likely to very slightly underestimate the numbers of Māori scientists within the academy. Furthermore, in the present paper we focus on Māori and Pasifika scientists in science faculties but there will also be Māori and Pasifika scientists in social science and humanities and health faculties, which will not be captured by the data reported below.
Māori are under-represented in science faculties at all of Aotearoa New Zealand’s eight universities (Table 1). University A had the highest level of representation, which may be attributed to the science faculty being combined with another discipline at this particular university (Table 1). From 2008 to 2018, University D has never employed a Māori academic in their science faculty (Table 1). Māori comprised less than 5% of the total FTEs in science faculties at all other universities between 2008 and 2018, the averages were 4.3, 1.4, 1.6, 3.7 and 0.6% respectively at University B, C, E, F and H (Table 1). Importantly, there were no significant differences between the percentage of Māori FTEs in 2008 and 2018 (paired t-test: t10 = −0.24, p = 0.82). Thus, meaning that over 11 years there has been no improvement in Māori representation in science faculties (Table 1).

Table 1. The percentage of Māori and Pasifika full-time equivalents (FTEs) of academic staff in science faculties at each of Aotearoa New Zealand’s eight universities. University A and G both have a combined faculty (i.e. science and another discipline) whereas all other universities have separate faculties and data is solely for science faculties. University E was unable to provide FTE data prior to 2011 and university H was only able to provide data from 2015.

CSVDisplay Table

Māori scientists employed in Aotearoa New Zealand’s crown-research institutes

Promisingly, and in contrast with patterns of Māori scientists at universities the percentage of Māori scientists (i.e. of the total headcount) employed by CRIs has increased from 2008 to 2018 at half (2/4) of the CRIs that were able to provide temporal data (Table 2). At CRI A, Māori comprised 1.8% of the scientists employed in 2008 and this steadily increased to 3.8% in 2018 (Table 2). Similarly at CRI B, the percentage of Māori scientists have increased from 3.4% to 7.8% respectively (Table 2). At CRI C, Māori have comprised between 0.01% and 0.03% of scientists employed over a period of 11 years and at CRI D it has varied between 0% and 0.6% (Table 2).

Table 2. The percentage of Māori and Pasifika scientists of the total headcount employed by each of Aotearoa New Zealand’s crown-research institutes. CRI E could only provide data for 2019 and CRI F only had data for new recruits from 2016–2018. CRI G did not contribute data to this research.

CSVDisplay Table
Certain CRIs are doing better than others, it is however important to note, particularly given CRIs outward espousals of commitments to and valuing ‘Māori research’ and mātauranga (i.e. GNS 2018), that Māori remain under-represented in all CRIs in Aotearoa New Zealand, including CRI A and B (Table 2). Additionally, the fact that three out of seven of the CRIs could not provide sufficient data suggests that these institutions have a lot of work to do in collecting data on the diversity of the staff that they employ.

Pasifika scientists employed in Aotearoa New Zealand’s universities and crown-research institutes

There is currently an absence of research into the experiences of Pasifika scientists in Aotearoa-New Zealand’s science system. However like Māori scientists, Pasifika scientists are likely to be marginalised and under-valued within the current science system. Pasifika scientists in both universities and CRIs are extremely under-represented (Tables 1 and 2). Notably of the 11 institutions (inclusive of universities and CRIs) that provided data only three reported having Pasifika representation exceeding 1% of either the total headcount or total number of FTEs in more than one year (Tables 1 and 2). Four institutions (one university and three CRIs) reported having employed zero Pasifika scientists for 11 consecutive years (Tables 1 and 2). Importantly, there were no significant differences between the percentage of Pasifika FTEs in universities in 2008 and 2018 (paired t-test: t8 = 0.36, p = 0.73). Thus, meaning that over 11 years there has been no improvement in Pasifika representation in science faculties (Table 2).
The patterns in the percentage of both Māori and Pasifika scientists employed at university G were very different from all other institutions (Table 1). Firstly, university G was the only university that in some years employed more Pasifika than Māori scientists (Table 1). In 2008, 7.4% of FTEs in the science faculty of university G belonged to Pasifika scientists, which was the highest recorded in all eight institutions over 11 years (Table 1). However, Pasifika scientists in this faculty had only 4.4 FTEs in 2008, meaning that 7.4% equated to five Pasifika staff (data not shown).

The diversity of scientists employed in science faculties in Aotearoa New Zealand’s universities

Between 2008 and 2018, the majority of academics in the Computing and Mathematical Sciences, Engineering and Science departments at university D were European comprising between 58.7% and 85.2% of the total FTEs (Figure 1(A)). University D distinguishes between ‘European’ and ‘New Zealand European/Pākehā’ and the data presented in Figure 1(A) suggests that not many academics in these departments associate with the latter group. Thus, suggesting that most academics employed within these departments are from overseas. In these departments (i.e. Computing and Mathematical Sciences, Engineering and Science) between 2008 and 2018 there was a consistent increase in the percentage of FTEs of Asian ethnicities (12.3% increase in Computing and Mathematical Sciences, 6.8% in Engineering, 2.4% in Science; Figure 1(A)).
Figure 1. (A) The percentage of full-time equivalents (FTEs) for each recorded ethnicity in three science faculties at university D in2008 and 2018 and (B) the percentage of Māori and Pasifika FTEs in those three faculties for academic staff from 2008–2018.
Note: In both the Engineering and Science departments there were no Māori or Pasifika employed between 2008 and 2018.
📷Display full size
The data provided by university D clearly illustrates a severe lack of Māori and Pasifika academic staff representation in sciences faculties (Figure 1(B)). It shows that in two of the three departments, there have never been any Māori academics employed (Figure 1(B)). Furthermore, in those three departments no Pasifika academic staff have been employed in 11 years (2008–2018). Māori academics have comprised 4.1%–7.5% of the total FTEs in the Computing and Mathematical Science department (Figure 1).
NZ European/Pākehā formed the majority (52.8%–63.6%) of academic staff employed in the science faculty of university B and this percentage has decreased by 11.8% between 2008 and 2018 (Figure 2). People who did not declare their ethnicity (unknown) comprised a small percentage (average = 3.2% of the total FTEs; Figure 2). European academics made up on average 20% of the total FTEs employed in this faculty between 2008 and 2018 (Figure 2). Māori and Pasifika scientists were under-represented, comprising on average 6.0% and 2.6% respectively (Figure 2). The percentage of Māori FTEs has decreased from 7.3% (2008) to 6.4% (2018), whereas the percentage Pasifika FTEs has increased from 2.0% to 4.8% over the 11-year period (2008–2018; Figure 2). However, there was no statistically significant difference between both Māori and Pasifika FTEs over time (p > 0.05).
Figure 2. The percentage offull-time equivalents (FTEs) for each recorded ethnicity at university B from 2008 to 2018.
Note: University B has a combined science faculty (i.e. science and another discipline).
📷Display full size
The importance of department by department analysis of universities ethnicity data is highlighted when comparing the percentage of Māori FTEs university-wide and the science faculty (Figure 3). The average percentage of Māori FTEs university wide at university F was 4.7% from 2008 to 2018, whereas it was consistently lower within the science faculty (Figure 3). Similarly, representation of Pasifika academics in the science faculty at university F was much lower compared to the entire university (Figure 4). The average between 2008 and 2018 was 1.5% of Pasifika FTEs across the university whereas it was only 0.4% in the science faculty (Figure 4).
Figure 3. The percentage of Māori full-time equivalents (FTEs) of academics in both the science facultyand across the entire university at university F.
Note: y axis is limited to 15%.
📷Display full size
Figure 4. The percentage of Pasifika full-time equivalents (FTEs) for academic staff in both the science faculty across the entire university at university F.
Note: The y axis is limited to 15%.
📷Display full size

The diversity of scientists employed in Aotearoa New Zealand’s crown-research institutes

CRI B was the only CRI that was able to provide relatively good quality, temporal data. Data from this institution indicated that African scientists made up approximately 1% of scientists employed from 2016 to 2018 and both Asian and Australian scientists have made up on average 5.4% and 5.0% respectively of the total headcount from 2008 to 2018 (Figure 5). The percentage of European scientists has increased steadily from 16.1% in 2008 to 23.5% in 2018 (Figure 5). The percentage of Māori scientists employed has also increased from 3.4% in 2008 to 7.8% in 2018 (Figure 5). Although this increase is promising, Māori remain under-represented within this institution. Interestingly, the percentage of NZ European/Pākehā employed at CRI B has decreased from 64.9% (2008) to 45.3% (2018; Figure 5). This may speak to the increasing value the science system places on international expertise, whereby scientists from overseas or with international experience are valued more than those from Aotearoa, which is driven in a large part by global ranking systems that value international staff recruitment (Stack 2016). This is driven largely by the increasing importance placed on international university ranking systems. Importantly, scientists coming from overseas will likely have very little understanding of things that are highly important within the context of Aotearoa (e.g. te Tiriti o Waitangi). Considering the data presented, urgent action is required to address this apparent selection of international scientists over Māori and Pasifika scientists. Rather than copying and pasting a blanket statement in job advertisements of empty words like the following: ‘The University of Canterbury actively seeks to meet its obligation under the Treaty of Waitangi Te Tiriti o Waitangi’ (UoC date unknown), CRIs and universities need to be actively recruiting Māori and Pasifika scientists and hence need to consider the following questions when hiring new staff:
  1. How is this person likely to contribute to the uplifting of Māori communities in a meaningful way?
  2. Do they have any experience working with Indigenous communities?
  3. What is their understanding of Te Tiriti o Waitangi and the Treaty of Waitangi?
  4. How do you see your role as supporting our institution's commitments to Pasifika communities?
Figure 5. Percentage of the total headcount for each recorded ethnicity at crown-research institute (CRI) B from 2008 to 2018.
Note: Ethnicity groups in this graph differ from previous graphs.
📷Display full size
CRI E were only able to supply data in the year that it was requested (i.e. 2019) due to their HR systems. In 2019, this particular CRI employed zero Pasifika scientists and 1.6% of scientists were Māori (Figure 6). The majority of scientists employed at CRI E in 2019 were NZ European/Pākehā (55.0% NZ European) and 21.5% were ‘European’ (Figure 6).
Figure 6. The percentage of the total headcount of each recorded ethnicity at crown-research institute (CRI) E in 2019.
Note: Ethnicity groupings differ from previous graphs.
📷Display full size
CRI F only began collecting ethnicity data, despite previously collecting gender data, in 2016. Their data was also only collected for new recruits. We were, therefore, unable to disaggregate science staff from general and non-science staff. From 2016 to 2018 the majority (59%–66%) of new recruits were ‘NZ Europeans’. In 2017, 14% of new recruits were Pasifika whereas in 2016 and 2018 there were no Pasifika recruits. Māori comprised between 2% of new recruits in 2017 and 2018 but 8% in 2016 (data not shown)...."
submitted by lolpolice88 to Maori [link] [comments]

2020.03.30 00:20 Denys_Picard When will the Media and the People in Position of Authority start telling the truth to Americans concerning the risk profile of Covid-19, instead of fomenting panics and hysteria.

When will the Media and the People in Position of Authority start telling the truth to Americans concerning the risk profile of Covid-19, instead of fomenting panics and hysteria.
It is something else to listen to the media again explaining they are doing a great job when every night all they do is feed anxiety of their audience, including parents and children. Shame on them all...again. Yes, again, because this is the new culture of the past 30 years, where every new knowledge on psychological manipulation quickly finds its way at the top of the Nightly News techniques.
For example, the numbers we hear every night, when they are not visually thrown in our faces. Let's try to bring some pondering to this.
Even some"specialists are being caught explaining the big dangers in the "death rate" of this virus..."for seasonal flu: Its is 0,1% and here for Covid-19 it is 1,5%, even higher in China, up to 3,5%..."
This is the ultimate of Fake news, something comitted out of naivete, from experts whom trust other experts, whom have spoken to a more bigger expert, etc...and sometime out of straight malicious intent.
These media "people" are comparing oranges and apples in the most classical style.
And I will try, so as to not add to the confusion, to take the example of the Seasonal flu to make things clear. From the Weekly U.S. Influenza Surveillance Report on the CDC website we can read:
From: Weekly U.S. Influenza Surveillance Report Week 12 2020 (take note that week 12 extended about 10 days because of the Covid situation). Further, this is week 40 of the whole Season, which starts of Sep 29 2019, and week 12 in 2020.
Now, in the top blue section you can read:
at Least 39 million flu illnesses;
400,000 Hospitalization;
24,000 Deaths from flu.
Illnesses must be understood strictly as an Estimation (it is also referred to as infection, infection rate, infection in the population, illness in the population). This is important, because just after they say Hospitalization, this is almost entirely a measured amount, very little is estimated. Then the Deaths (also often referred to in the literature as the fatalities, and of which some may be estimated and others counted).
Then in the table you see how much total tests where employed up to March 21st (Cumulative). That is 1,2 Million people where tested for the common seasonal flu. Of these, 242,330 tested positive. This results with a 20% positive rate of testing. The number you hear every night blasted into your eyes and ears is this last number, the tested positive cases, which they refer to simply as the "cases".
Now, you must understand that there is a technique the CDC employs to make the estimation of infected people in the population. They use a multiplier, this multiplier is sometime used against the tested positive cases, sometime against the hospitalization level.
An early estimation made by the CDC in 2009 concerning the Swine Flu H1N1 (2009) was a multiplier of 79. Estimates of the Prevalence of Pandemic (H1N1) 2009, United States, April–July 2009
"...23 July 2009, a total of 43,677 laboratory-confirmed cases, 5009 hospitalizations, and 302 deaths had been reported to the Centers for Disease Control and Prevention (CDC)..."
In this case, you multiplied 43,677 confirmed cases against the multiplier 79 = 3,450,000 estimated illnesses.
That is Cases confirmed x Multiplier = Estimated illnesses (or infections) in the general population.
By the end of the crisis, this multiplier had increased quite a bit. Because the last Illness estimation was 61,8 million infections and 240,000 hospitalization. Doing the reverse calculation we get:
61,800,000/240,000 = 257...this was the end multiplier. So, from the July cumulative data of 2009, the multiplier was 79, but by the end of the crisis in may 2010, this multiplier increase about 3 fold to become 257.
An intermediate multiplier from data up to October went as follows:
From the CDC report "2009 H1N1-Related Deaths, Hospitalizations and Cases:Details of Extrapolations and Ranges: United States,Emerging Infections Program (EIP) Data
AS you can read: Calculate cases (which here is the wrong expression and it creates confusion, it should read: Calculate illnesses or infections): Multiply Hospitalization by 221,79.
So right here, is where the CDC often uses the term Cases when they should use Illness or Infections, because in most reports when they use cases, it means diagnosed confirmed cases, not estimation in the population of illness. Fauci himself is often caught in this confusing and misleading language.
You can see for yourself from the same document, they estimated cases by November 2009 to be 14 to 34 millions. Clearly this is the Illness level, not the confirmed cases.
Now, the whole media is stuck up in this hysterics by making you believe that you have 1 chance in 50 of dying or so if you catch the virus...that is not true. In fact, you had, for H1N1 less probability of dying from H1N1 than from the seasonal flu by a ratio of 3 to 1.
Currently, neither the CDC nor the WHO have calculated a Multiplier for Covid-19. The Total rate of Hospitalization is difficult to have. On the 25 of March, Andrew Cuomo, in a press conference live on NPR explained that about 1 in 5 confirmed cases is hospitalized currently. On that day, The larger NY City area had about 25,000 cases confirmed, which mean they had about 5,000 hospitalization.
A paper earlier this month from the CDC Mortality and Morbidity Weekly: Severe Outcomes Among Patients with Coronavirus Disease 2019 (COVID-19) an early report when they were 4229 confirmed cases across the US, had a rate of hospitalization of 58% (2,449 out of 4229).
Some independent papers have calculated tentative Multipliers. It's the case of the paper which was used for the structure of the article in the NYTimes of March 20th "Coronavirus Could Overwhelm U.S. Without Urgent Action, Estimates Say". The paper is entitled "Substantial undocumented infection facilitates the rapid dissemination of novel coronavirus (SARS-CoV2)" The problem with the NYTimes is that they used color saturation excessively to terrorize people, like they do with Hurricane maps, when they want the people to listen. They don't say how the authors of the paper contributed to its use by the NYTimes. The NYTimes explains that they used their database of Covid pandemics from US cases.But the 500,000 new cases per day appears an exageration...But the scientific paper is excellent, it uses a complex statistical mathematics modeling which is explained over 30 pages Supplemental data.
There might be some short comings, like there always is in these kinds of work, firstly because many parameters must be estimated. And further, for example, some of the tools they use make the assumptions of Gaussian distributions, when progressively it is understood that epidemics, like the weather and networking are non-linear distributions. For example, they use what is called an Ensemble Adjustment Kalman Filter. A model developed in2008, as an adjustment or refinement on the original 1950 Ensemble Kalman Filter. But in climate science,which obeys to laws where the 4 parameters of a distribution are mobile, the Gaussian has only 2 of these, the Mean and the Variance, while the 2 others Kurtosis and Asymmetry are fixed at 2 and 0 respectively.
Parameters of the Standard Normal Distribution are (2,0,1,0), Kurtosis (alpha α ), Skewness (Beta β), Standard Deviation (Sigma σ ) and Mean (Mu µ) respectively.
From the wikipedia Stable Distribution page:
The Stable Distribution has always been the parent of most probability distributions, the normal distribution just being a special case of it. The Stable distribution s very complex, to me also, and many attempts have been made over the decades to make integration in many linear models so they perform with better likelihood. Recently, an academic paper has created a more pragmatic approach to it : "...this paper attempts to present a uniform analytical approximation for the stable distribution based on matching power series expansions. For this solution, the trans-stable function is defined as an auxiliary function to evaluate the stable distribution." Asymptotic Expansions of the Stable distribution.
For a graphic represention of the fluidity of the Stable Distribution in regard to the sensitivity of a change in its papameters, you can go to John P Nolan's page.
Recentely, to better adapt the Kalman Filter, climate science has created the Local Ensemble Adjusted Kalman Filter which makes use of of outlayers instead of doing data softening. Network Theory also has made adaptation to take into account the non-linearity of dynamics modeling. Epidemiology is a close relative of both Climatology and Network theory.
Non-Gaussian statistics in global atmospheric dynamics: a study with a 10 240-member ensemble Kalman filter using an intermediate atmospheric general circulation model :
"This study also discusses how many ensemble members are necessary to represent a non-Gaussian PDF (probability distribution function) without contamination due to the sampling error, as higher-order non-Gaussian statistics are generally more vulnerable to the sampling error due to a limited ensemble size."
From the result of their paper, you can infer a multiplier, they used the Wuhan Proving data of Confirmed cases, which stood on the 24 of January at 370. From there, they estimated that 13,118 people were infected in the population. So 13,118/370 = 35. Would this be a reliable multiplier to use against the confirmed cases in the is very difficult to say.
Another original statistical approach is by Tian Hao,Infection Dynamics of Coronavirus Disease 2019 (Covid-19) Modeled with the Integration of the Eyring's Rate Process Theory and Free Volume Concept
I won't explain his approach, but is a short cut approach when too many parameters must be evaluated. His multiplier is 10.
Using the Spreadsheet available at the CDC on the REED's model for Multiplicator, I obtained a multiplier of 110, when confirmed cases were 19,240 in the US. My feeling was that this was a bit high, I thought, from reading quite a bit that would be between 40 and 90. But I made some assumptions myself for lack of information.
But these multiplier may give you an idea of what is the real fatality risk of Covid 19.
For example, with a multiplier of 10, today March 29, with 125,000 cases in the US and about 2000 fatalities, you first calculate the Case Fatality Rate (CFR): 2,000/125,000 = 1,6%.
Then you calculate the Infection Mortality Rate, dividing the CFR by the multiplier:
1,6% / 35 = 0,046%
1,6% / 10 = 0,16%
So, you sit between a bit more than the Seasonal Flu, to half of it.
But these numbers may be misleading because a pandemic is something unique for which there is no empirical data, unlike the seasonal flu. Further, because of the massive rapid distribution, a different time of reactions of authorities, the different social structures where the virus may settle...Chronic events statistics, such as those of the seasonal flu, and those of exceptional event, may not really compare and may misinform. What is important to understand, is that the number of cases you see in the media for Covid-19 do reflect the amount of people infected in the population. So the case mortality rate doesn't reflect your probability of death if you catch the SARS-CoV2 virus.
The WHO explained this in detail in the Situation Report 46 (note here they use the term Crude, from the British, instead of Case: but they o use Infection Mortality Rate):
"Mortality for COVID-19 appears higher than for influenza, especially seasonal influenza. While the true mortality of COVID-19 will take some time to fully understand, the data we have so far indicate that the crude mortality ratio (the number of reported deaths divided by the reported cases) is between 3- 4%, the infection mortality rate (the number of reported deaths divided by the number of infections) will be lower. For seasonal influenza, mortality is usually well below 0.1%. However, mortality is to a large extent determined by access to and quality of health care."
It's the same thing with the new expression so popular among the Press, the Reproduction Rate (or Replication Rate). They say it's so high...blablabla...The thing is that they forget to mention that the Replication rate always starts high and then falls shortly towards, and eventually below, 1.
It's not that this situation doesn't call for attention, and action, it is just the tone which is irresponsible by many people in position of authority. We are not helping ourselves by being anxious, and projecting this anxiety on our environment and loved ones.
For example, an early estimation of the H1N1 virus Pandemic was for:

  • US pop: 300 million
  • Cases (read illness or infection): 46 million (37 - 55 million)
  • Hospitlizations: 2.8 million (2 - 3.6 million)
  • On ventilators: 132,000 (228,000 - 454,000)
  • Deaths: 192,000 (126,000 - 226,000)
Zilberberg MD, Sandrock C, Shorr A. Swine origin influenza A (H1N1) virus and ICU capacity in the U.S.: are we prepared? In: PLoS Currents: Influenza. August 22, 2009. (Accessed October 22, 2009, at opens in new tab.)
In the end there were:
61,8 Million illnesses
240,000 Hospitalization
12,470 deaths
A departure, but early forecasts often suffer from this wide excesses. But, lets contain, lets mitigate, and let's not panic...and tell your media they should learn to speak English or French instead of Anxiety, music having always been my first language (as a listener of course)...
submitted by Denys_Picard to u/Denys_Picard [link] [comments]

Data Off - New Tamil Short Film 2019 - YouTube Did you know? SQL Server 2008 SSRS New Data Visualizations SQL Server Express 2008 R2 Tutorial 4 - Insert And Select Data Standard Deviation of Grouped Data - YouTube How to open an .MDF file? (Attach a Database in SQL Server) Store Data in Database from VB 2008 Form - YouTube Windows Server 2008 Backup (Step-By-Step) - YouTube

GitHub - AFRL-RY/data-unicorn-2008: Synthetic Aperture ...

  1. Data Off - New Tamil Short Film 2019 - YouTube
  2. Did you know? SQL Server 2008 SSRS New Data Visualizations
  3. SQL Server Express 2008 R2 Tutorial 4 - Insert And Select Data
  4. Standard Deviation of Grouped Data - YouTube
  5. How to open an .MDF file? (Attach a Database in SQL Server)
  6. Store Data in Database from VB 2008 Form - YouTube
  7. Windows Server 2008 Backup (Step-By-Step) - YouTube

Windows Server 2008 (and 2008 R2) Backup has been given an extensive overhaul and improved with a large number of new features. In this video, Tyler Johnson ... This statistics video tutorial explains how to calculate the standard deviation of grouped data. It discusses how to calculate the mean and the standard devi... For more Details visit The new Gauge data region is most often used to provide a high-level summary of your data by highlighting key performance indicator (KPI) values. The gauge uses a pointer to show a single value ... Watch #DataOff New Tamil Short Film 2019 Cast and crew Cast - THAMIZH , MADHAVI Director - LOGAN Dop - UMASUDHAN NANDHAGOPAL Music - UDHAY Editing - A KUMARA... What is a database without data? This tutorial shows how to insert data and select data from the database. The commands used ar... This video shows how to attach a database in SQL Server 2008 R2. It covers a common Access Denied error and provides solutions. At the end, it briefly covers...